450 results about "Relation graph" patented technology

The invention discloses an energy consumption model intelligent construction system and method based on artificial intelligence, and relates to the technical field of artificial intelligence, and the system comprises an Internet of Things multi-source data collection module, a data cleaning and space-time calibration module, a multi-source data semantic fusion module, a dynamic energy consumption relation graph construction module, an intelligent decision engine module and an edge-cloud collaborative deployment module. According to the method, multi-source data are fused through a Transform multi-head self-attention mechanism, a dynamic energy consumption relation graph is constructed by using a graph neural network, and dynamic modeling and intelligent regulation and control of energy consumption are realized in combination with an edge-cloud hierarchical decision architecture; the method comprises the steps of data acquisition and standardization, cleaning calibration, semantic fusion, graph modeling, hierarchical decision making and collaborative execution. According to the method, the problems of insufficient data integration and model staticization of a traditional system are solved, the accuracy, real-time performance and global optimization capability of energy consumption management are improved, the method is suitable for scenes such as intelligent buildings, the energy efficiency is remarkably improved, and the data security is guaranteed.

The invention relates to the technical field of defect detection, in particular to a cross-process defect root cause tracing method and system. According to the method, the data feature matrix covering multiple dimensions is formed by integrating the process parameters, the equipment state and the quality detection information, so that the performance evaluation of each process is more comprehensive, the interaction and influence paths among the processes can be clearly described by constructing the process relation graph, and the performance evaluation efficiency is improved. Meanwhile, basic data support is provided for quantifying the relation between the procedures through introduction of procedure attenuation factors, the shortest propagation path and the propagation probability of the defects can be accurately recognized by analyzing a procedure relation graph, root cause tracing of the cross-procedure defects becomes systematized in combination with construction of a knowledge graph, and the defect tracing efficiency is improved. The knowledge graph not only can effectively integrate and display data, but also is convenient for quickly positioning problems, and by utilizing an adaptive correlation analysis technology, the system can intelligently adjust an analysis model and a path and continuously optimize a defect detection and tracing process when facing new data.

The invention relates to a water quality prediction method and system based on gating residual enhancement and feature fusion, and belongs to the technical field of water environment intelligent analysis and deep learning. Taking each water quality index as a node of the graph, and constructing two complementary variable relation graph structures by utilizing a Pearson's correlation coefficient and mutual information; respectively inputting the two graph structures into a graph convolutional network, extracting deep dependency features among indexes, and splicing and fusing the deep dependency features. A multi-head attention mechanism is used as a trunk to extract global time dependence, a GRU network is introduced to extract local time sequence features, GRU output is used as an adjustable residual term to be injected into the attention trunk through a residual gating mechanism, self-adaptive enhancement of local dynamic features is achieved, and finally a self-adaptive fusion mechanism is introduced to generate comprehensive representation. According to the method, the complex dependency relationship between the water quality indexes and the time dynamic evolution process can be modeled in a collaborative manner, the response capability to key local change and sudden change events is remarkably enhanced, and the accuracy and robustness of water quality prediction are improved.

The invention provides a project management software document uploading method based on OCR and a large language model, and relates to the technical field of large language models, and the method comprises the steps: receiving a project document picture, and carrying out OCR recognition through a multi-scale feature pyramid and an attention-enhanced convolutional neural network; inputting the text information into a pre-trained large language model for classification and key field extraction; determining a data partitioning strategy according to the item type to create a storage space; establishing a multi-dimensional index structure to store project information and analyzing a content logic relationship; and generating and displaying a project information relation graph. According to the invention, intelligent identification, classification and correlation analysis of the project documents are realized, and the project management efficiency and the data utilization value are improved.

The invention relates to the technical field of database management, in particular to a two-way linkage database table and supervision submission form field synchronous construction method which is applied to a supervision data submission scene. According to the scheme, the method comprises the steps that physical structure metadata of a database table and definition metadata of a supervision submission form are obtained, a semantic vector set is formed by combining metadata semantic analysis and semantic coding, and a bidirectional mapping relation graph is constructed through relation weaving; constructing a linkage propagation path according to a field change event, realizing adaptive bidirectional structure adjustment, generating a field synchronous construction scheme, and driving bidirectional mapping relation graph evolution optimization through feedback learning; according to the method, the bidirectional mapping relation graph and self-adaptive bidirectional structure adjustment are creatively combined, and efficient and self-adaptive bidirectional field synchronous construction is realized.

The invention discloses a park management method based on digital twinning, particularly relates to the field of park operation, and is used for solving the problem that a multi-source event is difficult to stably chain under the conditions of compensation delay and evidence gap and causes misalignment of disposal triggering. Comprising the following steps of: registering an event in a pool to generate an event number and writing the event number into a platform arrival moment; screening and mapping an anchor point event to a park object identifier and a process stage identifier to generate a management token; checking a stage sequence and an evidence item requirement set according to an event chain rule table in event chain construction and generating a conflict point list; the delay portrait library generates a credible mark when a source is generated and forms a reverse sequence violation spectrum; the digital twin object relation graph forms an influence domain span mark; the closed table is returned to obtain a disposal convergence guarantee level; the cost-sensitive decision tree outputs a disposal template identifier, and the disposal template table generates a management action instruction and a trigger voucher and records an instruction acceptance identifier; and returning, comparing and freezing the exception management token, and updating the event chain rule table and the delay portrait library.

The invention relates to the technical field of databases, and discloses a multi-type database performance optimization and operation and maintenance management method and system.The multi-type database performance optimization and operation and maintenance management method comprises the steps that real-time performance indexes of a heterogeneous database cluster are collected, and a noise reduction data set is obtained; generating a cross-library performance coupling degree matrix through the resource competition coupling degree and the data dependence coupling degree; constructing a coupling relation graph; identifying a bottleneck node set based on the coupling relation graph and the abnormal level; generating an optimization strategy; and performing strategy conflict identification according to the optimization strategy, generating a processing strategy, and executing the processing strategy optimization strategy. Through the improved weighting centrality algorithm, the core bottleneck node which has the greatest influence on the whole cluster is accurately identified, a differential optimization strategy is adopted according to the database type, a perfect strategy conflict detection and avoidance mechanism is established, mutual interference in the optimization process is effectively prevented, the optimization success rate is improved, and the optimization efficiency is improved. And the optimization time is shortened.

The invention, which relates to the technical field of ecological environment monitoring and intelligent management, discloses a real-time monitoring and early warning management system based on an ecological environment, comprising a multi-modal data acquisition module for acquiring and preprocessing multi-medium environment data, a multi-medium risk conduction modeling module for constructing a directed graph, and a dynamic modeling risk conduction process. The cross-medium risk conduction chain effect evaluation system has the advantages that the problem of cross-medium risk conduction chain effect evaluation deficiency is effectively solved, multi-modal data provides a data basis, the modeling module constructs a directed conduction relation graph, and the model construction module constructs a three-dimensional model and simulates pollutant migration and transformation and risk effects, and the intelligent early warning module generates early warnings, so that the cross-medium risk conduction chain effect evaluation system has the advantages that the cross-medium risk conduction chain effect evaluation deficiency problem is effectively solved; a graph neural network is used for dynamic modeling, a conduction path is presented, a digital twin engine simulates pollutant migration and transformation and a risk conduction effect, an intelligent early warning module carries out multi-level evaluation and timely early warning, a decision intervention module generates an optimal intervention scheme, and the system realizes accurate evaluation and effective response of cross-medium risk conduction.

The invention relates to the technical field of data fusion, and discloses an animal scene-oriented adaptive multi-modal data fusion method, which comprises the following steps of: extracting spatio-temporal characteristics from multi-source heterogeneous data such as visual sense, auditory sense and physiological sensing, constructing an animal-environment-group ternary spatio-temporal relation graph, and constructing an animal-environment-group ternary spatio-temporal relation graph; a pilot frequency sampling problem is solved through an adaptive interpolation algorithm, cross-modal projection alignment is completed in a public semantic space, unified space-time representation is output, and confidence coefficient weight is dynamically calculated based on uncertainty measurement of each modal feature. According to the method, accurate alignment of multi-modal data is realized through the cross-modal space-time attention network, the multi-modal feature alignment error is reduced compared with that of a traditional LSTM method, the training data volume of a federated element migration reinforcement learning framework is reduced compared with that of a traditional migration learning method, and the cross-species generalization performance of the model is improved. A multi-level causal inference engine quantitatively reveals causal association between environmental factors and animal diseases, and in combination with a dynamic decision tree visualization technology, the decision recognition degree is improved.

The invention discloses an enterprise big data security early warning method based on anomaly detection, and the method comprises the following steps: S1, collecting original data, and carrying out the format unification and structure standardization processing; s2, preprocessing is carried out, and feature vectors are constructed; s3, a behavior entity relation graph is constructed, a graph attention network is adopted for training, and structural features in a normal behavior mode are learned; s4, calculating the deviation degree between the current behavior and the normal behavior; s5, reconstructing the feature vector, measuring the deviation degree between the current behavior and the standard behavior distribution by using a mahalanobis distance, and calculating the posterior anomaly probability of the behavior through a Bayesian updating mechanism; and S6, evaluating the risk level of the current behavior according to the posterior anomaly probability, and generating a corresponding early warning event. According to the method, the graph attention network and the Bayesian self-coding technology are fused, enterprise behavior anomaly detection and grading early warning are achieved, and the method has the advantages of being high in recognition precision, high in self-adaption and timely in response.

The invention discloses a network space map surveying and mapping method and system based on multi-source data fusion, and the method comprises the steps: obtaining a multi-source data set in a unified format based on network flow data, equipment information data and geographic position data; obtaining a network asset entity and an incidence relation graph thereof through entity identification and correlation analysis based on the multi-source data set with the uniform format; obtaining a network space three-dimensional map model through three-dimensional space mapping and visual rendering based on the network asset entity and the association relationship map thereof; based on the network space three-dimensional map model, performing dynamic updating according to the accessed real-time data flow to obtain real-time network space situation data; and obtaining a network security risk assessment result through anomaly detection and threat identification based on the real-time network space situation data. According to the invention, visual display and security situation awareness of the network space are realized, and a brand new decision support tool is provided for network security management.

The invention discloses a software development result traceability analysis system based on version control, and relates to the technical field of intelligent software analysis. According to the method, the non-tampering property of code submission records is ensured through a block chain technology, a credible basis is provided for traceability, the code semantic analysis module generates a data set containing semantic association in combination with a clustering algorithm and weighted calculation, and the function influence positioning module constructs a function association graph by applying a graph neural network algorithm, so that the traceability is improved. An influence path of code change on system functions is automatically identified, the time cost of complex project function influence positioning is remarkably reduced, an evolution path generation module analyzes and tracks a code evolution path in combination with a time sequence, and a core module identification module clusters key nodes and positions a core function module. And the time-tracing source data storage module generates a visual relation graph and a queried data set, so that the whole-process management from code submission to result tracing is realized.

The invention discloses a risk assessment method and system based on topology analysis, and relates to the technical field of risk assessment, and the method comprises the following steps: analyzing a communication relation between nodes, extracting a plurality of propagation paths between any two nodes, and forming a path set; mapping the path set based on the risk transmission direction and the path attribute, and constructing a multi-path propagation relation graph; based on the multi-path propagation relation graph, identifying a risk aggregation node with multi-path risk input, and fusing multi-path attributes to calculate a comprehensive risk value of the node; and generating network risk distribution based on the comprehensive risk value in combination with the operation state data. According to the method, the multi-path propagation topology model is constructed, and the risks on multiple paths are subjected to convergence analysis and comprehensive quantification in combination with the node operation state and the path interaction characteristics, so that the problem that the overall risk of the network is underestimated only by depending on a single dominant path in the prior art is solved.

The invention provides an scRNA-seq data clustering method, system and device based on ZINB distribution and graph attention. The scRNA-seq data clustering system mainly comprises three core modules: a ZINB auto-encoder, which is used for modeling scRNA-seq data based on zero-expansion negative binomial distribution, generating robust potential representation through a denoising auto-encoder, and accurately capturing sparsity, excessive discreteness and shedding events of gene expression; the residual image attention auto-encoder is used for constructing a cell relation graph by using a Pearson correlation coefficient, dynamically learning a neighborhood weight in combination with a multi-head attention mechanism, retaining original features through residual connection, and relieving the excessive smoothness problem of image convolution; and a deep clustering model is self-optimized: target distribution and soft label distribution are minimized through KL divergence, and end-to-end joint optimization of embedded learning and clustering is realized.

The invention discloses an enterprise innovation risk assessment system based on virtual-real fusion common knowledge learning, and the system comprises a data collection and preprocessing module which is used for collecting and preprocessing a real operation standardized data set; the simulation environment construction and disturbance generation module is used for constructing a virtual simulation environment and generating a virtual data set; the semantic alignment and fusion module is used for performing semantic alignment and weighted fusion to generate a virtual-real fusion data set; the risk relation graph construction module is used for constructing an enterprise innovation risk relation graph; the multi-order structure embedding module is used for executing multi-order graph structure embedding; the common knowledge migration module is used for extracting a common feature structure and executing migration adaptation; and the risk assessment module is used for carrying out risk assessment. According to the method, enterprise atlas and multi-semantic structure embedding are fused, cross-enterprise risk migration assessment is achieved, and the method has the advantages of being high in expression ability, high in adaptability and accurate in assessment.

The invention provides a multi-view structure learning method based on multi-expert cooperation, and relates to the technical field of graph neural networks and multi-view learning, and the method comprises the steps: reconstructing initial multi-view data, and obtaining multi-view structure data; respectively training the single-view expert model and the shared expert model by utilizing a first loss function and a second loss function based on the multi-view structure data to obtain a trained single-view expert model and a trained shared expert model; constructing a collaborative decision model by using the trained single-view expert model and the trained shared expert model; training the collaborative decision model by using a third loss function to obtain a trained collaborative decision model; and analyzing the graph data by using the trained collaborative decision model to obtain a joint decision result, and completing learning of the multi-view structure. According to the method, the problems of large structural noise interference, isolated expert model information and insufficient node classification accuracy when an existing graph neural network processes multi-relation graph data are solved.

The invention discloses a software development management method based on big data, and relates to the technical field of software development management, and the method comprises the steps: collecting multi-source development data, transmitting the multi-source development data to a data lake in real time through a distributed message queue, and generating a structured data stream with a timestamp and a time sequence relation graph; performing Monte Carlo sampling on the three-dimensional radiation field model of the neural radiation field and the causal influence graph, simulating the cascade influence submitted by the current code, and outputting a risk value and a dependency chain; loading a neural radiation field three-dimensional code space model, a node risk value and a dependency chain, dynamically rendering a causal influence path, and generating a visual risk report; and generating a code optimization task according to the visual risk report, submitting an optimized code version to a code library, and calibrating parameters of the neural radiation field three-dimensional code space model. According to the method, the spatio-temporal characteristics of code change are analyzed, Monte Carlo simulation is combined, hidden dependence and a risk conduction chain are identified, and the comprehensiveness and predictive ability of framework corruption analysis are improved.

The invention discloses an unbiased scene graph generation method for relieving long-tail distribution. The method comprises the following steps: S1, constructing a model; s2, data preprocessing; s3, object feature extraction; s4, constructing a graph learning structure (GLS); s5, a regional message passing network (RMPN); s6, generating a pseudo label; s7, defining a loss function; s8, performing model training; s9, generating a pseudo tag and a triple; and S10, carrying out iterative training and optimization. According to the unbiased scene graph generation method for relieving long-tail distribution, the correlation between entities is calculated by utilizing GLS, the relation graph is optimized, the relation representation of head and tail categories is enhanced, meanwhile, the RMPN improves the semantic representation of objects and relations through an information transmission mechanism, pseudo labels are generated on the basis of unlabeled relations in a training set through a pseudo label generation mechanism, and the robustness of the unbiased scene graph generation method for relieving long-tail distribution is improved. And in combination with a high-confidence screening mechanism, generating a learning sample of a pseudo-triple enhanced tail category.

The invention relates to the technical field of data processing, in particular to a contract text key payment index extraction method based on natural language processing, which comprises the following steps of: performing structured chapter division on a contract text, constructing a chapter hierarchical tree, and extracting a key payment index of the contract text according to a matching relationship between multi-level path nodes of the chapter hierarchical tree and chapter title keywords, setting a candidate path containing payment information; extracting an action entity of each contract text in the candidate path, and extracting a correlation index corresponding to the action entity according to a condition clause and a condition type of the action entity; extracting adjacent words based on the relevance index of the action entity, and constructing a word relation graph by analyzing the part-of-speech features of the adjacent words and the current action entity; iterating the word relation graph according to a preset association rule, and correcting each node in the word relation graph; outputting a key payment index corresponding to the contract text according to the difference part of the word relation graph before and after correction; accuracy and efficiency of payment index extraction are realized.

The invention provides a public space dynamic risk network assessment method fusing spatio-temporal characteristics, which relates to the technical field of network assessment, and comprises the steps of acquiring basic data, adaptively dividing grid units by adopting an octree algorithm, establishing a connection relation graph, calculating a risk propagation probability by utilizing a graph neural network, and calculating a risk propagation path by applying an improved A * algorithm. And finally outputting risk level distribution, propagation path risk change and prevention and control deployment suggestions. According to the invention, refined quantitative evaluation of public space risks is realized, and risk early warning accuracy and prevention and control measure pertinence are improved.

The invention discloses a financial knowledge graph construction method and system based on artificial intelligence, and relates to the field of artificial intelligence data processing. The method comprises the following steps: performing multi-dimensional semantic analysis on a heterogeneous financial data source, and extracting a structured semantic fragment; constructing a financial entity perception unit, identifying a multi-granularity entity and generating a unique code; generating a preliminary relation graph based on the event cascade relation and the attachment structure, and injecting a semantic translation label; normalizing the atlas relationship through semantic separation and a label reconstruction mechanism to form a financial relationship network with consistent semantics; executing evolution increment iteration in combination with the newly added corpus, and dynamically updating nodes and edge sets; and performing semantic consistency and structural integrity evaluation on an iteration result, and outputting a stable financial knowledge graph structural body. By introducing a multi-factor semantic analysis model, a causal relationship modeling mechanism and a graph evolution iteration strategy, systematic improvement of the financial knowledge graph in the aspects of structural expression precision, semantic reasoning ability and dynamic adaptability is achieved.

A document-level intelligent manufacturing process flow relation extraction method comprises the following steps: S1, acquiring a process document, and labeling process entities in the process document and a relation between the process entities; s2, performing deep coding on the process document by using a pre-training language model, extracting core semantic information of the process document, and generating process entity representation with consistent semantics based on an entity aggregation strategy of context sensing; s3, nodes and edges of a heterogeneous graph are constructed according to process entity representation, and a relation graph convolutional network R-GCN and a hierarchical attention mechanism are introduced to fully capture the relation between process entities; according to the method, the modeling capability of technological process sequence constraint and the fusion effect of document semantics and graph structure features are respectively enhanced by a sequential construction strategy and a process perception double-graph distillation mechanism oriented to a manufacturing process, the integrity and the utilization rate of information transmission are improved, the performance loss caused by error layer-by-layer propagation is relieved, and the method is suitable for popularization and application. Accurate and efficient extraction of the entity relationship in the process document is realized.

The invention relates to the technical field of artificial intelligence, and provides a subway network traffic prediction method based on association modeling, comprising: acquiring a heterogeneous data source of a target subway network; the heterogeneous data sources are cleaned, aligned and fused, and a time-space association data set is constructed; based on the subway network topology and the real-time passenger flow state, constructing a dynamic relation graph representing the dynamic interaction between the line and the station; the space-time correlation data set and the dynamic relation graph are utilized to cooperatively train a space-time prediction module and a relation reasoning module in an alternate optimization mode, and the relation reasoning module iteratively updates an edge weight in the dynamic relation graph through a graph attention mechanism and a space-time convolution operation; and based on the dynamic relation graph and the optimized space-time prediction module, carrying out multi-step prediction on the passenger flow in the future period and outputting a prediction uncertainty quantitative index. According to the technical scheme of the application, the accuracy and reliability of subway passenger flow prediction are significantly improved by fusing multi-source data and dynamically modeling the site association relationship.

The invention discloses a scientific and technological achievement analysis and prediction method and system based on big data, and relates to the technical field of machine learning and big data analysis, and the method comprises the steps: collecting and preprocessing multi-source scientific and technological achievement semantic data, and constructing a scientific and technological concept relation graph; the method comprises the following steps: performing training by taking a time sequence diagram convolutional network as a basic framework and taking a scientific and technological concept relation graph as a training sample, constructing a dynamic knowledge flow semantic model, performing evolution feature extraction on the scientific and technological concept relation graph by utilizing the dynamic knowledge flow semantic model, and outputting a knowledge flow feature vector; and inputting the causal enhanced space-time diagram into a space-time diagram neural network, aggregating semantic association and causal relationships among the scientific and technological achievements in a space dimension, capturing a dynamic change mode of scientific and technological achievement characteristics in a time dimension, and outputting a scientific and technological concept time sequence predicted value sequence. According to the method, the causal enhancement space-time diagram is constructed, so that trend deduction and causal traceability analysis are carried out for the time dimension, and the accuracy of scientific and technological achievement development trend prediction is improved.

The invention discloses a software development project progress prediction management method based on artificial intelligence, and relates to the technical field of project management, and the method comprises the steps: extracting a causal relation from entity relation graph data, and constructing a causal knowledge graph; a graph structure analysis method and a time sequence prediction method are combined, predicted completion time and delay probability are calculated for the causal knowledge graph and the associated time sequence data, and a progress risk assessment result is generated; performing causal chain identification on the progress risk assessment result and the causal knowledge graph by adopting a causal reasoning method to generate delay interpretation information; according to the delay interpretation information, a preset resource priority and a task weight, a rule driving and priority scheduling strategy is adopted to construct a compensation scheduling scheme; and implementing a compensation scheduling scheme, collecting implementation effect data and analyzing a scheduling effect through a closed-loop feedback and dynamic adjustment mechanism, and generating an optimized scheduling management scheme. The intelligent level and the practical value of project progress management are greatly improved.

The invention relates to the technical field of knowledge graph completion, provides an implicit relation perception time sequence knowledge graph completion method based on dynamic embedding and self-attention, and aims to improve the inference and completion capability of missing facts in a time sequence knowledge graph. According to the method, time evolution modeling, a graph neural network and semantic similarity calculation are combined, and dynamic embedding representation fusing static, trend and periodic characteristics is constructed. Explicit structure information is extracted through a multilayer relational graph convolutional network, and meanwhile, an implicit semantic similarity relationship under synchronous and asynchronous time is introduced to construct a sparse semantic graph. Structural information and semantic information are fused through GRU, multi-time step features are aggregated by adopting a time perception self-attention mechanism, and key time information is highlighted. And finally, entity prediction is completed by using a ConvTransE decoder, and the model is optimized through cross entropy loss. According to the method, a static structure and implicit semantics can be modeled at the same time, the time sensitivity is enhanced, and the method is suitable for large-scale dynamic graph completion and has better reasoning ability and generalization performance.

The invention discloses a recommendation system-oriented high-concealment poisoning attack detection method and application, and the method comprises the following steps: S1, user behavior and relationship modeling: constructing a user feature vector and symbiotic relationship graph, and describing user scoring behavior preference and a co-occurrence relationship; s2, importance pre-screening: based on similarity measurement and importance modeling of score distribution, filtering out normal users weakly related to potential attack users; s3, cross-graph relation decoupling: carrying out key relation extraction and dynamic and static relation separation on the user relation graph, and obtaining high-quality relation representation through a cross-graph fusion mechanism; and S4, double-hyper-sphere cooperative detection: normal user representation is restrained by using a concentric hyper-sphere shell, and abnormal user detection is realized through the degree of deviation from the boundary. According to the method, high-concealment poisoning attacks can be effectively detected in a real recommendation system environment, the detection accuracy is remarkably improved, the false alarm rate is reduced, and the method has good practicability and robustness.

The invention discloses a block chain multi-level fragmentation load balancing method and system based on a predictive subgraph, and aims to solve the problems of load imbalance and frequent cross-fragmentation transaction in the existing block chain fragmentation technology. According to the scheme, a transaction relation graph is constructed in a block chain system; performing sub-graph division on the constructed transaction relation graph in each sub-graph division period; the target of division is to obtain N sub-graphs, so that the sum of edge weights of each sub-graph is maximized, and meanwhile, the number of nodes in the sub-graphs meets a preset threshold value. And executing a fragmentation division process aiming at the divided sub-graphs, and carrying out hierarchical management by adopting a multi-stage fragmentation strategy. A sub-graph attribution prediction process is executed before a sub-graph division cycle is started; and a time sequence prediction method is adopted to predict the affiliation of the subgraph to which the node belongs, and sampling correction is carried out on the edge based on a prediction result.

The invention relates to the technical field of Internet of Things security, in particular to an Internet of Things vulnerability data restoration system, which is used for solving the problems that in the prior art, multi-dimensional vulnerability features cannot be accurately extracted and propagated, potential vulnerabilities cannot be efficiently identified, high-risk vulnerabilities cannot be preferentially processed according to a dynamic resource adjustment strategy, and the vulnerability data restoration efficiency cannot be improved. And the accuracy, the automation level and the response efficiency of vulnerability management are reduced. According to the method, a heterogeneous relation graph between equipment and components is constructed through the vulnerability identification and positioning module, multi-dimensional vulnerability features are accurately extracted and propagated, efficient identification of potential vulnerabilities is realized, priority ranking is performed in combination with CVSS scores and equipment importance, high-risk vulnerabilities are preferentially processed through scientific scores and a dynamic resource adjustment strategy, and the efficiency of vulnerability identification is improved. And the to-be-fixed vulnerability list containing multi-aspect information is generated, so that the vulnerability management accuracy, the automation level and the response efficiency are remarkably improved.

The invention relates to an enterprise financial analysis method and system. The enterprise financial analysis method comprises the steps of obtaining structured financial data and unstructured market data of an enterprise; preprocessing the structured financial data and the unstructured market data; generating a joint embedded representation by using the pre-processed structured financial data and the pre-processed non-structured market data; constructing an enterprise relation graph, and generating a dynamic enterprise relation graph based on the enterprise relation graph; obtaining enterprise financial data; an LSTM-TCN mixed time sequence model is constructed; performing pre-training on the LSTM-TCN mixed time sequence model by using the macroeconomic fluctuation data; performing fine adjustment on the pre-trained LSTM-TCN mixed time sequence model by using enterprise financial data; and inputting the current enterprise financial data into the fine-tuned LSTM-TCN mixed time sequence model for risk assessment. According to the method and the device, enterprise risks caused by factors such as unstructured market data can be found in time.